Automatic Machine Learning (AutoML)

F. Hutter (1) , Balázs Kégl (2, 3, 4, 5) , R. Caruana (6) , I. Guyon (7) , H. Larochelle (8) , E. Viegas (6)

1 University of Freiburg [Freiburg]
2 AppStat
3 LAL - Laboratoire de l'Accélérateur Linéaire
4 LRI - Laboratoire de Recherche en Informatique
5 TAO - Machine Learning and Optimisation
6 Microsoft Research
7 Chalearn
8 UdeS - Université de Sherbrooke

The success of machine learning in many domains crucially relies on human machine learning experts, who select appropriate features, workflows, machine learning paradigms, algorithms, and their hyperparameters. The rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML. For example, a recent instantiation of AutoML we’ll discuss is the ongoing ChaLearn AutoML challenge (http://codalab.org/AutoML).

Domaines: Machine Learning [stat.ML]

F. Hutter, Balázs Kégl, R. Caruana, I. Guyon, H. Larochelle, et al.. Automatic Machine Learning (AutoML) . ICML 2015 Workshop on Resource-Efficient Machine Learning, 32nd International Conference on Machine Learning, Jul 2015, Lille, France. ⟨in2p3-01171463⟩